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391	Workshop: Momentum of a quantum particle and the Heisenberg uncertainty principle Quantum aspects of physical chemistry http://quantum.bu.edu/courses/PLTL/3/3.pdf Last updated Tuesday, November 15, :08:42-05:00 Co Add to Reading List Source URL: quantum.bu.edu Language: English - Date: 2005-11-17 16:47:39 Quantum mechanics Uncertainty principle Momentum operator Position operator Wave function Expectation value Matrix mechanics Introduction to eigenstates
392	Latent Outlier Detection and the Low Precision Problem Fei Wang, Sanjay Chawla and Didi Surian University of Sydney Add to Reading List Source URL: www.outlier-analytics.org Language: English - Date: 2013-08-13 08:05:46 Linear algebra Matrix theory Multivariate statistics Robust statistics Non-negative matrix factorization Outlier Singular value decomposition
393	ON THE LINEAR INDEPENDENCE OF SPIKES AND SINES JOEL A. TROPP Abstract. The purpose of this work is to survey what is known about the linear independence of spikes and sines. The paper provides new results for the case wh Add to Reading List Source URL: users.cms.caltech.edu Language: English - Date: 2008-04-16 22:00:34 Operator theory Matrix Partial differential equations Complex analysis Functional analysis Harmonic analysis Differential forms on a Riemann surface NeumannPoincar operator
394	MATEC Web of Conferences 30, DOI: m atec conf 0 5 C Owned by the authors, published by EDP Sciences, 2015 Effect of Silicon Nitride Incorporation on Microstructure and Hardne Add to Reading List Source URL: www.matec-conferences.org Language: English Nitrides Superhard materials Metal plating Chemical elements Native element minerals Plating Silicon nitride Nanocomposite Nickel Titanium nitride Metal matrix composite Silicon
395	http://www.r-project.org/ A (very) short introduction to R and do the following (assuming you work on a Add to Reading List Source URL: cran.r-project.org Language: English - Date: 2014-03-18 06:24:32 Linear algebra Array programming languages Vectors Functional languages Vector calculus R Euclidean vector Matrix Vector space Norm Vector Command-line interface
396	ORDINARY LEAST SQUARES REGRESSION MATH 196, SECTION 57 (VIPUL NAIK) Corresponding material in the book: For the computational process, Section 5.3 of the book is relevant. For comparing notation: • Add to Reading List Source URL: files.vipulnaik.com Language: English - Date: 2016-08-13 11:33:29 Regression analysis Estimation theory Parametric statistics Econometrics Least squares Linear regression Ordinary least squares Linear algebra Matrix Vector space Line Linear least squares
397	M-‐OSRP technical presentations at the 2013 SEG International Conference and Convention, and post-‐convention Workshops. Monday, Sept Add to Reading List Source URL: www.mosrp.uh.edu Language: English - Date: 2015-05-20 13:16:41 Multiple Algorithm Pi Matrix Arthur Weglein
398	HP Prime Technology Corner 27 The Practice of Statistics for the AP Exam, 5e Section 11-2, P. 706 Add to Reading List Source URL: www.hpprime.de Language: English - Date: 2014-09-09 06:17:49 Programmable calculators Graphing calculators Tap Matrix HP calculators Calculator HP Prime TAP Portugal Statistical hypothesis testing
399	On the Performance Prediction of BLAS-based Tensor Contractions Elmar Peise, Diego Fabregat-Traver, and Paolo Bientinesi AICES, RWTH Aachen {peise,fabregat,pauldj}@aices.rwth-aachen.de Add to Reading List Source URL: www.dcs.warwick.ac.uk Language: English - Date: 2014-11-13 12:51:32 Numerical linear algebra Tensors Numerical software Basic Linear Algebra Subprograms GPGPU Cache Tensor contraction Tensor Matrix CPU cache Kernel Compute kernel
400	Chapter 6 Constructs, Components, and Factor models Parsimony of description has been a goal of science since at least the famous dictum commonly attributed to William of Ockham to not multiply entities beyond necessity Add to Reading List Source URL: personality-project.org Language: English - Date: 2011-08-17 18:07:44 Covariance and correlation Factor analysis Multivariate statistics Regression analysis Linear algebra Principal component analysis Eigenvalues and eigenvectors Covariance matrix Matrix Exploratory factor analysis Singular value decomposition Multiple correlation

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